Open AccessMotor Imagery EEG Classification based on Machine Learning Algorithm
Author(s) -
Pradeep Rusiya,
N. S. Chaudhari
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f7754.038620
Subject(s) - brain–computer interface , motor imagery , computer science , interface (matter) , electroencephalography , artificial intelligence , signal (programming language) , feature extraction , process (computing) , motor system , field (mathematics) , noise (video) , signal processing , pattern recognition (psychology) , psychology , neuroscience , computer hardware , mathematics , bubble , maximum bubble pressure method , parallel computing , pure mathematics , image (mathematics) , programming language , operating system , digital signal processing
The advancement of computer technology facilitates in the field of medical science for the analysis of complex Diseases related to the neurology. These technologies named as BCI (brain computer interface). The BCI is open area of research for physically challenge people such as paralyzed and amputees. The current technology of computer interface interest in EEG (electroencephalographic) for the analysis of signals for the predication of nervous system. The current trends of brain computer interface focus on process signal of EEG for the sense of human body behaviors and movement of nervous system, motor imagery and various senses. The gathered signal by the EEG is very noisy and predication and recognition of the motor imagery is typical. The minimization of noise upgrades the predication procedure and examination of signal behaviors. For the analysis of behaviors system utilized soft computing processing approach, for example, neural system, optimization techniques. The component extraction and feature selection are also major issue in motor imagery analysis for critical and complex disorder of human brain system.